Challenges in QA can vary depending on the source of knowledge, the answer extraction strategy employed, and the domain of the question. Machine Reading Comprehension (MRC) is one such challenge in QA, that requires the conversational QA (ConvQA) agent to understand a given open-domain text and thereafter answer question/s in conversation about it. These questions are often not paraphrased and may co-reference previous queries. The required answer may be a span of the given text or free-form. When the machine comprehension dialog involves multiple co-referenced questions such that a latter question may be a logical successor of the former, the challenge is termed as Conversational Machine Comprehension (CMC). A lot of research in MRC revolves around single-turn QA, but multi-turn CMC also holds major relevance because humans seek information conversationally by asking follow-up questions for additional information based on what they have already learned. Still, the inherent complexity involved in dealing with text comprehension and reasoning over dialogs and context had kept CMC as a far-fetched goal. However, the recent success in achieving at-par-with-human performance on single-turn MRC models [1]} due to the advancement in natural language understanding and modeling [2]}, [3]}, [4]}, and the introduction of large-scale conversational datasets CoQA [5]} and QuAC [6]} have made information-seeking dialogs possible.
